


'''simple docstring'''


import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
				from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   __snake_case       ,   __snake_case=13       ,   __snake_case=32       ,   __snake_case=3       ,   __snake_case=4       ,   __snake_case=[10, 20, 30, 40]       ,   __snake_case=[2, 2, 3, 2]       ,   __snake_case=True       ,   __snake_case=True       ,   __snake_case=37       ,   __snake_case="gelu"       ,   __snake_case=10       ,   __snake_case=0.02       ,   __snake_case=["stage2", "stage3", "stage4"]       ,   __snake_case=[2, 3, 4]       ,   __snake_case=None       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= parent
											_SCREAMING_SNAKE_CASE					:			str							= batch_size
											_SCREAMING_SNAKE_CASE					:			int							= image_size
											_SCREAMING_SNAKE_CASE					:			Any							= num_channels
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_stages
											_SCREAMING_SNAKE_CASE					:			List[str]							= hidden_sizes
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= depths
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= is_training
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= use_labels
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			List[str]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			str							= num_labels
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Any							= out_features
											_SCREAMING_SNAKE_CASE					:			Any							= out_indices
											_SCREAMING_SNAKE_CASE					:			Tuple							= scope






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

											_SCREAMING_SNAKE_CASE					:			Dict							= None
											if self.use_labels:
															_SCREAMING_SNAKE_CASE					:			str							= ids_tensor([self.batch_size]       ,   self.num_labels    )

											_SCREAMING_SNAKE_CASE					:			str							= self.get_config()

											return config, pixel_values, labels






							def UpperCAmelCase_      (       self    ):
											return ConvNextVaConfig(
											    num_channels=self.num_channels       ,   hidden_sizes=self.hidden_sizes       ,   depths=self.depths       ,   num_stages=self.num_stages       ,   hidden_act=self.hidden_act       ,   is_decoder=__snake_case       ,   initializer_range=self.initializer_range       ,   out_features=self.out_features       ,   out_indices=self.out_indices       ,   num_labels=self.num_labels       ,   )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= ConvNextVaModel(config=__snake_case    )
											model.to(__snake_case    )
											model.eval()
											_SCREAMING_SNAKE_CASE					:			List[str]							= model(__snake_case    )
											# expected last hidden states: B, C, H // 32, W // 32
											self.parent.assertEqual(
											    result.last_hidden_state.shape       ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,   )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= ConvNextVaForImageClassification(__snake_case    )
											model.to(__snake_case    )
											model.eval()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= model(__snake_case       ,   labels=__snake_case    )
											self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= ConvNextVaBackbone(config=__snake_case    )
											model.to(__snake_case    )
											model.eval()
											_SCREAMING_SNAKE_CASE					:			List[str]							= model(__snake_case    )

											# verify hidden states
											self.parent.assertEqual(len(result.feature_maps    )       ,   len(config.out_features    )    )
											self.parent.assertListEqual(list(result.feature_maps[0].shape    )       ,   [self.batch_size, self.hidden_sizes[1], 4, 4]    )

											# verify channels
											self.parent.assertEqual(len(model.channels    )       ,   len(config.out_features    )    )
											self.parent.assertListEqual(model.channels       ,   config.hidden_sizes[1:]    )

											# verify backbone works with out_features=None
											_SCREAMING_SNAKE_CASE					:			int							= None
											_SCREAMING_SNAKE_CASE					:			str							= ConvNextVaBackbone(config=__snake_case    )
											model.to(__snake_case    )
											model.eval()
											_SCREAMING_SNAKE_CASE					:			Tuple							= model(__snake_case    )

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps    )       ,   1    )
											self.parent.assertListEqual(list(result.feature_maps[0].shape    )       ,   [self.batch_size, self.hidden_sizes[-1], 1, 1]    )

											# verify channels
											self.parent.assertEqual(len(model.channels    )       ,   1    )
											self.parent.assertListEqual(model.channels       ,   [config.hidden_sizes[-1]]    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Any							= config_and_inputs
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= {"""pixel_values""": pixel_values}
											return config, inputs_dict






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			str							= config_and_inputs
											_SCREAMING_SNAKE_CASE					:			List[Any]							= {"""pixel_values""": pixel_values, """labels""": labels}
											return config, inputs_dict





@require_torch
class        lowercase__    (		_snake_case	,	_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: Optional[Any]       =    (
							    (
							        ConvNextVaModel,
							        ConvNextVaForImageClassification,
							        ConvNextVaBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							A_				: Union[str, Any]       =    (
							    {"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							A_				: int       =    False
							A_				: Union[str, Any]       =    False
							A_				: List[str]       =    False
							A_				: Optional[int]       =    False
							A_				: List[str]       =    False






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= ConvNextVaModelTester(self    )
											_SCREAMING_SNAKE_CASE					:			List[Any]							= ConfigTester(self       ,   config_class=__snake_case       ,   has_text_modality=__snake_case       ,   hidden_size=37    )






							def UpperCAmelCase_      (       self    ):
											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()






							def UpperCAmelCase_      (       self    ):
											return






							@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""    )
							def UpperCAmelCase_      (       self    ):
											pass






							@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""    )
							def UpperCAmelCase_      (       self    ):
											pass






							@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""    )
							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											if not self.model_tester.is_training:
															return

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Any							= self.model_tester.prepare_config_and_inputs_with_labels()
															_SCREAMING_SNAKE_CASE					:			int							= True

															if model_class.__name__ in [
															    *get_values(__snake_case    ),
															    *get_values(__snake_case    ),
															]:
																			continue

															_SCREAMING_SNAKE_CASE					:			Any							= model_class(__snake_case    )
															model.to(__snake_case    )
															model.train()
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self._prepare_for_class(__snake_case       ,   __snake_case       ,   return_labels=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= model(**__snake_case    ).loss
															loss.backward()






							def UpperCAmelCase_      (       self    ):
											if not self.model_tester.is_training:
															return

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			int							= self.model_tester.prepare_config_and_inputs_with_labels()
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= False
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= True

															if (
															    model_class.__name__
															    in [*get_values(__snake_case    ), *get_values(__snake_case    )]
															    or not model_class.supports_gradient_checkpointing
															):
																			continue

															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= model_class(__snake_case    )
															model.to(__snake_case    )
															model.gradient_checkpointing_enable()
															model.train()
															_SCREAMING_SNAKE_CASE					:			Tuple							= self._prepare_for_class(__snake_case       ,   __snake_case       ,   return_labels=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			str							= model(**__snake_case    ).loss
															loss.backward()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			str							= self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE					:			int							= model_class(__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= inspect.signature(model.forward    )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_SCREAMING_SNAKE_CASE					:			int							= [*signature.parameters.keys()]

															_SCREAMING_SNAKE_CASE					:			int							= ["""pixel_values"""]
															self.assertListEqual(arg_names[:1]       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case    )






							def UpperCAmelCase_      (       self    ):
											def check_hidden_states_output(__snake_case       ,   __snake_case       ,   __snake_case    ):
															_SCREAMING_SNAKE_CASE					:			int							= model_class(__snake_case    )
															model.to(__snake_case    )
															model.eval()

															with torch.no_grad():
																			_SCREAMING_SNAKE_CASE					:			List[Any]							= model(**self._prepare_for_class(__snake_case       ,   __snake_case    )    )

															_SCREAMING_SNAKE_CASE					:			Dict							= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															_SCREAMING_SNAKE_CASE					:			Tuple							= self.model_tester.num_stages
															self.assertEqual(len(__snake_case    )       ,   expected_num_stages + 1    )

															# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]    )       ,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,   )

											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= True
															check_hidden_states_output(__snake_case       ,   __snake_case       ,   __snake_case    )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															_SCREAMING_SNAKE_CASE					:			List[str]							= True

															check_hidden_states_output(__snake_case       ,   __snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Any							= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case    )






							@slow
							def UpperCAmelCase_      (       self    ):
											for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE					:			List[Any]							= ConvNextVaModel.from_pretrained(__snake_case    )
															self.assertIsNotNone(__snake_case    )







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
				return image







@require_torch
@require_vision
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							@cached_property
							def UpperCAmelCase_      (       self    ):
											return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""    ) if is_vision_available() else None






							@slow
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""    ).to(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= self.default_image_processor
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= prepare_img()
											_SCREAMING_SNAKE_CASE					:			List[Any]							= preprocessor(images=__snake_case       ,   return_tensors="""pt"""    ).to(__snake_case    )

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE					:			str							= model(**__snake_case    )

											# verify the logits
											_SCREAMING_SNAKE_CASE					:			Tuple							= torch.Size((1, 1000)    )
											self.assertEqual(outputs.logits.shape       ,   __snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.tensor([0.9996, 0.1966, -0.4386]    ).to(__snake_case    )
											self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,   __snake_case       ,   atol=1e-4    )    )








'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_						:  int				  =						logging.get_logger(__name__)


if is_vision_available():
				import PIL





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: int       =    ["""pixel_values"""]






							def __init__(       self       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = PILImageResampling.BICUBIC       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = True       ,   __snake_case = 1 / 255       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = True       ,   **__snake_case       ,   ):
											super().__init__(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= size if size is not None else {"""shortest_edge""": 224}
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											_SCREAMING_SNAKE_CASE					:			Any							= get_size_dict(__snake_case       ,   default_to_square=__snake_case       ,   param_name="""crop_size"""    )

											_SCREAMING_SNAKE_CASE					:			str							= do_resize
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= size
											_SCREAMING_SNAKE_CASE					:			List[str]							= resample
											_SCREAMING_SNAKE_CASE					:			str							= do_center_crop
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= crop_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= do_rescale
											_SCREAMING_SNAKE_CASE					:			Dict							= rescale_factor
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= do_normalize
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= image_std if image_std is not None else OPENAI_CLIP_STD
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_convert_rgb






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = PILImageResampling.BICUBIC       ,   __snake_case = None       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			int							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											if "shortest_edge" not in size:
															raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
											_SCREAMING_SNAKE_CASE					:			Any							= get_resize_output_image_size(__snake_case       ,   size=size["""shortest_edge"""]       ,   default_to_square=__snake_case    )
											return resize(__snake_case       ,   size=__snake_case       ,   resample=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= get_size_dict(__snake_case    )
											if "height" not in size or "width" not in size:
															raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""    )
											return center_crop(__snake_case       ,   size=(size["""height"""], size["""width"""])       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											return rescale(__snake_case       ,   scale=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											return normalize(__snake_case       ,   mean=__snake_case       ,   std=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = ChannelDimension.FIRST       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_resize if do_resize is not None else self.do_resize
											_SCREAMING_SNAKE_CASE					:			Any							= size if size is not None else self.size
											_SCREAMING_SNAKE_CASE					:			List[str]							= get_size_dict(__snake_case       ,   param_name="""size"""       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Any							= resample if resample is not None else self.resample
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= do_center_crop if do_center_crop is not None else self.do_center_crop
											_SCREAMING_SNAKE_CASE					:			str							= crop_size if crop_size is not None else self.crop_size
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= get_size_dict(__snake_case       ,   param_name="""crop_size"""       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_rescale if do_rescale is not None else self.do_rescale
											_SCREAMING_SNAKE_CASE					:			int							= rescale_factor if rescale_factor is not None else self.rescale_factor
											_SCREAMING_SNAKE_CASE					:			Tuple							= do_normalize if do_normalize is not None else self.do_normalize
											_SCREAMING_SNAKE_CASE					:			List[str]							= image_mean if image_mean is not None else self.image_mean
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_std if image_std is not None else self.image_std
											_SCREAMING_SNAKE_CASE					:			Any							= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= make_list_of_images(__snake_case    )

											if not valid_images(__snake_case    ):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

											if do_resize and size is None:
															raise ValueError("""Size must be specified if do_resize is True."""    )

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

											if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= [convert_to_rgb(__snake_case    ) for image in images]

											# All transformations expect numpy arrays.
											_SCREAMING_SNAKE_CASE					:			str							= [to_numpy_array(__snake_case    ) for image in images]

											if do_resize:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [self.resize(image=__snake_case       ,   size=__snake_case       ,   resample=__snake_case    ) for image in images]

											if do_center_crop:
															_SCREAMING_SNAKE_CASE					:			Tuple							= [self.center_crop(image=__snake_case       ,   size=__snake_case    ) for image in images]

											if do_rescale:
															_SCREAMING_SNAKE_CASE					:			List[Any]							= [self.rescale(image=__snake_case       ,   scale=__snake_case    ) for image in images]

											if do_normalize:
															_SCREAMING_SNAKE_CASE					:			Dict							= [self.normalize(image=__snake_case       ,   mean=__snake_case       ,   std=__snake_case    ) for image in images]

											_SCREAMING_SNAKE_CASE					:			str							= [to_channel_dimension_format(__snake_case       ,   __snake_case    ) for image in images]

											_SCREAMING_SNAKE_CASE					:			int							= {"""pixel_values""": images}
											return BatchFeature(data=__snake_case       ,   tensor_type=__snake_case    )




